{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('username: 15010492066', '0,')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import jaqs\n",
    "from jaqs.data import DataApi\n",
    "\n",
    "api = DataApi(addr=\"tcp://data.quantos.org:8910\")\n",
    "\n",
    "api.login(\"15010492066\", \"eyJhbGciOiJIUzI1NiJ9.eyJjcmVhdGVfdGltZSI6IjE1MTMwNTA0ODY3MTQiLCJpc3MiOiJhdXRoMCIsImlkIjoiMTUwMTA0OTIwNjYifQ.s_530n20Aelg7-8-1IorTgoa_yp6goImz20hhGIzMGU\") \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_data, msg = api.query(\n",
    "    view = 'lb.secDailyIndicator', \n",
    "    filter=\"start_date=20050404&end_date=20050404&symbol=600519.SH\",\n",
    "    fields='total_mv,float_mv,pe_ttm,pb,trade_date'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>float_mv</th>\n",
       "      <th>pb</th>\n",
       "      <th>pe_ttm</th>\n",
       "      <th>symbol</th>\n",
       "      <th>total_mv</th>\n",
       "      <th>trade_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>561672.683</td>\n",
       "      <td>5.0825</td>\n",
       "      <td>26.2616</td>\n",
       "      <td>600519.SH</td>\n",
       "      <td>1963890.5</td>\n",
       "      <td>20050404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     float_mv      pb   pe_ttm     symbol   total_mv trade_date\n",
       "0  561672.683  5.0825  26.2616  600519.SH  1963890.5   20050404"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               float_mv       pb    pe_ttm      total_mv trade_date\n",
      "symbol                                                             \n",
      "000001.SZ  1.972637e+07   0.9906    8.6337  2.002070e+07   20180322\n",
      "000002.SZ  3.027318e+07   2.9577   14.4312  3.442008e+07   20180322\n",
      "000008.SZ  1.803303e+06   3.1824   46.5438  1.986923e+06   20180322\n",
      "000060.SZ  2.169079e+06   2.3620   22.8355  2.334574e+06   20180322\n",
      "000063.SZ  1.076898e+07   3.2094   28.7822  1.314822e+07   20180322\n",
      "000069.SZ  5.114528e+06   1.4226    8.0981  6.825796e+06   20180322\n",
      "000100.SZ  4.281065e+06   1.7229   25.0389  4.932965e+06   20180322\n",
      "000157.SZ  2.633359e+06   0.8744   28.6456  3.296882e+06   20180322\n",
      "000166.SZ  9.994031e+06   1.6917   24.1699  1.124544e+07   20180322\n",
      "000333.SZ  3.617770e+07   5.2416   22.1720  3.741429e+07   20180322\n",
      "000338.SZ  3.472415e+06   1.6823   11.6057  6.429780e+06   20180322\n",
      "000402.SZ  2.984398e+06   1.0664    8.6688  2.985941e+06   20180322\n",
      "000413.SZ  3.333025e+06   1.5337   31.8563  4.664424e+06   20180322\n",
      "000415.SZ  1.819502e+06   1.1556   13.3277  3.611760e+06   20180322\n",
      "000423.SZ  3.991860e+06   4.0560   19.5309  3.992801e+06   20180322\n",
      "000425.SZ  2.860368e+06   1.2028   34.9076  2.866161e+06   20180322\n",
      "000503.SZ  3.634837e+06  28.8566    0.0000  3.634837e+06   20180322\n",
      "000538.SZ  1.053889e+07   6.0231   33.2979  1.053897e+07   20180322\n",
      "000540.SZ  3.178410e+06   2.1978   13.5776  3.452784e+06   20180322\n",
      "000559.SZ  2.371416e+06   4.9797   28.5112  2.375977e+06   20180322\n",
      "000568.SZ  8.026546e+06   5.7276   34.6755  8.387173e+06   20180322\n",
      "000623.SZ  2.268372e+06   1.1857   12.8767  2.408097e+06   20180322\n",
      "000625.SZ  4.456644e+06   1.2282    6.8104  5.691138e+06   20180322\n",
      "000627.SZ  1.073391e+06   2.0874   22.0763  3.917919e+06   20180322\n",
      "000630.SZ  2.571763e+06   1.6582   58.9354  2.831637e+06   20180322\n",
      "000651.SZ  2.979275e+07   5.1205   15.2746  3.001850e+07   20180322\n",
      "000671.SZ  2.547770e+06   2.1600   21.4042  3.179308e+06   20180322\n",
      "000686.SZ  1.874703e+06   1.1872   23.5386  1.874703e+06   20180322\n",
      "000709.SZ  3.928252e+06   0.8566   21.1272  3.928885e+06   20180322\n",
      "000723.SZ  6.011719e+05   3.0303   19.5071  2.282898e+06   20180322\n",
      "...                 ...      ...       ...           ...        ...\n",
      "601788.SH  4.988854e+06   1.2094   19.4176  5.887976e+06   20180322\n",
      "601800.SH  1.511869e+07   1.3090   12.1043  2.081688e+07   20180322\n",
      "601818.SH  1.672039e+07   0.8056    7.0377  2.204546e+07   20180322\n",
      "601857.SH  1.303473e+08   1.2347   62.5932  1.473319e+08   20180322\n",
      "601866.SH  2.490687e+06   2.5569   17.1187  3.668501e+06   20180322\n",
      "601872.SH  1.869485e+06   1.3510   31.6698  2.098585e+06   20180322\n",
      "601877.SH  4.318245e+06   2.8426   19.0368  5.462477e+06   20180322\n",
      "601878.SH  4.413334e+05   3.2658   41.4979  4.413333e+06   20180322\n",
      "601881.SH  1.239588e+06   1.5942   20.5083  1.022849e+07   20180322\n",
      "601888.SH  1.113887e+07   7.9379   44.3279  1.113887e+07   20180322\n",
      "601898.SH  4.905472e+06   0.7984   29.4341  7.106644e+06   20180322\n",
      "601899.SH  7.222338e+06   3.0721   40.0279  1.052527e+07   20180322\n",
      "601901.SH  5.243849e+06   1.4010   36.0909  5.243849e+06   20180322\n",
      "601919.SH  4.734118e+06   3.0063   30.8980  6.334090e+06   20180322\n",
      "601933.SH  6.678102e+06   4.9084   54.5012  9.819294e+06   20180322\n",
      "601939.SH  7.713301e+06   1.2019    8.4151  2.010088e+08   20180322\n",
      "601958.SH  2.313475e+06   1.8140  213.9167  2.313475e+06   20180322\n",
      "601966.SH  7.193856e+05   2.5195   21.1541  2.188800e+06   20180322\n",
      "601985.SH  2.977674e+06   2.5225   24.3972  1.095806e+07   20180322\n",
      "601988.SH  8.788922e+07   0.8886    7.0040  1.227597e+08   20180322\n",
      "601989.SH  9.970384e+06   1.4498  137.4894  1.242373e+07   20180322\n",
      "601991.SH  3.817846e+06   1.4753   25.3033  6.152103e+06   20180322\n",
      "601992.SH  4.023867e+06   1.1443   17.4630  5.274819e+06   20180322\n",
      "601997.SH  1.827474e+06   1.3719    7.4844  3.390423e+06   20180322\n",
      "601998.SH  2.150408e+07   0.9044    7.7485  3.298205e+07   20180322\n",
      "603160.SH  2.055084e+06  12.1720   39.9556  4.064273e+06   20180322\n",
      "603799.SH  7.404113e+06  14.1716   64.8246  7.493211e+06   20180322\n",
      "603833.SH  5.769890e+05   9.5381   49.5676  5.846291e+06   20180322\n",
      "603858.SH  1.730314e+06   2.8482   21.9296  3.598540e+06   20180322\n",
      "603993.SH  1.194334e+07   5.3421   98.6316  1.991450e+07   20180322\n",
      "\n",
      "[300 rows x 5 columns] 0,\n"
     ]
    }
   ],
   "source": [
    "# 获得HS300指数20180322的成份股\n",
    "df, msg = api.query(\n",
    "    view = 'lb.indexCons', \n",
    "    filter=\"index_code=000300.SH&start_date=20180322&end_date=20180322\"\n",
    ")\n",
    "symbols = \",\".join(df['symbol'])\n",
    "\n",
    "# 获得HS300成份股的日估值数据\n",
    "df_data, msg = api.query(\n",
    "    view = 'lb.secDailyIndicator', \n",
    "    filter=\"start_date=20180322&end_date=20180322&symbol=\"+symbols, \n",
    "    fields='total_mv,float_mv,pe_ttm,pb,trade_date'\n",
    ")\n",
    "df_data.set_index('symbol', inplace=True)\n",
    "print(df_data, msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import time\n",
    "today = time.strftime('%Y%m%d',time.localtime(time.time()))\n",
    "\n",
    "# 获取交易日历\n",
    "trade_date, msg = api.query(\n",
    "                view=\"jz.secTradeCal\", \n",
    "                fields=\"trade_date,istradeday, isweekday, isweekend, isholiday\", \n",
    "                filter=\"start_date=20050408&end_date=\" + today, \n",
    "                data_format='pandas')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20050408 : 79.27721557093423\n",
      "20050411 : 79.31779268292684\n",
      "20050412 : 76.44803832752626\n",
      "20050413 : 78.52498881118875\n",
      "20050414 : 76.95335174825173\n",
      "20050415 : 74.77283986013988\n",
      "20050418 : 75.60145228070171\n",
      "20050419 : 76.51487263157901\n",
      "20050420 : 75.45642631578953\n",
      "20050421 : 72.22156549295768\n",
      "20050422 : 34.10737703180212\n",
      "20050425 : 35.91959679715302\n",
      "20050426 : 37.59573082437275\n",
      "20050427 : 37.52860467625897\n",
      "20050428 : 38.487375636363616\n",
      "20050429 : 38.201029197080324\n",
      "20050509 : 55.958099272727274\n",
      "20050510 : 56.56790145454547\n",
      "20050511 : 56.089388000000014\n",
      "20050512 : 55.91266509090908\n",
      "20050513 : 56.02978545454546\n",
      "20050516 : 55.66706145454547\n",
      "20050517 : 56.11306909090914\n",
      "20050518 : 56.745004363636376\n",
      "20050519 : 56.78389236363637\n",
      "20050520 : 56.91260581818185\n",
      "20050523 : 55.21293163636363\n",
      "20050524 : 56.340300727272734\n",
      "20050525 : 56.20342509090911\n",
      "20050526 : 55.46462654545457\n",
      "20050527 : 55.446919272727236\n",
      "20050530 : 56.05052254545455\n",
      "20050531 : 55.751626545454535\n",
      "20050601 : 55.30547927272724\n",
      "20050602 : 54.83327745454545\n",
      "20050603 : 54.44473963636362\n",
      "20050606 : 54.7495218181818\n",
      "20050607 : 54.81453527272728\n",
      "20050608 : 59.081369818181834\n",
      "20050609 : 59.95971672727271\n",
      "20050610 : 58.48989163636365\n",
      "20050613 : 58.76488981818182\n",
      "20050614 : 57.54320727272725\n",
      "20050615 : 55.969236363636384\n",
      "20050616 : 56.33482254545455\n",
      "20050617 : 56.26656763636366\n",
      "20050620 : 57.93637672727274\n",
      "20050621 : 57.12760581818183\n",
      "20050622 : 56.97376072727274\n",
      "20050623 : 55.99293454545455\n",
      "20050624 : 56.150574181818186\n",
      "20050627 : 57.63562981818182\n",
      "20050628 : 56.409086909090924\n",
      "20050629 : 55.94105709090912\n",
      "20050630 : 54.903150909090925\n",
      "20050701 : 48.46956512455516\n",
      "20050704 : 46.555583629893256\n",
      "20050705 : 45.114515658363\n",
      "20050706 : 44.792814946619245\n",
      "20050707 : 44.28908932384343\n",
      "20050708 : 43.167651601423515\n",
      "20050711 : 42.44352953736655\n",
      "20050712 : 44.44200035587189\n",
      "20050713 : 43.723419928825614\n",
      "20050714 : 43.79003096085411\n",
      "20050715 : 42.649437010676145\n",
      "20050718 : 42.0447338078292\n",
      "20050719 : 42.40909893238436\n",
      "20050720 : 42.50654021352313\n",
      "20050721 : 42.38740071174378\n",
      "20050722 : 43.66350142348753\n",
      "20050725 : 44.0239153024911\n",
      "20050726 : 45.078787544484015\n",
      "20050727 : 46.20175373665483\n",
      "20050728 : 45.84602277580072\n",
      "20050729 : 45.52555800711745\n",
      "20050801 : 45.7607053380783\n",
      "20050802 : 46.803381428571406\n",
      "20050803 : 47.18994250000004\n",
      "20050804 : 47.36364035714287\n",
      "20050805 : 48.385466428571405\n",
      "20050808 : 48.82806857142857\n",
      "20050809 : 49.32245892857143\n",
      "20050810 : 49.83216321428571\n",
      "20050811 : 50.18783249999997\n",
      "20050812 : 49.8998264285714\n",
      "20050815 : 51.07557535714287\n",
      "20050816 : 50.72238464285715\n",
      "20050817 : 51.46616821428574\n",
      "20050818 : 36.78275964285715\n",
      "20050819 : 66.15861392857147\n",
      "20050822 : 66.43502107142862\n",
      "20050823 : 66.01765964285713\n",
      "20050824 : 66.5222385714286\n",
      "20050825 : 63.73340719424465\n",
      "20050826 : 64.02251371841159\n",
      "20050829 : 72.99646702898545\n",
      "20050830 : 73.03949890510951\n",
      "20050831 : 75.91471021897812\n",
      "20050901 : 77.25267518248165\n",
      "20050902 : 77.5503912408759\n",
      "20050905 : 78.12086386861316\n",
      "20050906 : 76.75050656934307\n",
      "20050907 : 77.99837043795625\n",
      "20050908 : 78.13593613138684\n",
      "20050909 : 77.48371313868613\n",
      "20050912 : 77.49666496350369\n",
      "20050913 : 79.40793065693437\n",
      "20050914 : 79.95178722627735\n",
      "20050915 : 80.49973868613137\n",
      "20050916 : 82.47516423357662\n",
      "20050919 : 82.87399343065692\n",
      "20050920 : 82.62345145985395\n",
      "20050921 : 81.49190729927014\n",
      "20050922 : 79.95381350364958\n",
      "20050923 : 78.78323357664235\n",
      "20050926 : 78.05326350364962\n",
      "20050927 : 77.66583722627738\n",
      "20050928 : 77.63541204379558\n",
      "20050929 : 78.22049233576644\n",
      "20050930 : 78.6120846715328\n",
      "20051010 : 77.65204671532842\n",
      "20051011 : 78.57468175182481\n",
      "20051012 : 78.79553467153289\n",
      "20051013 : 78.12032810218977\n",
      "20051014 : 77.32179635036493\n",
      "20051017 : 77.17193540145983\n",
      "20051018 : 78.07938759124089\n",
      "20051019 : 77.52210000000007\n",
      "20051020 : 78.25512226277378\n",
      "20051021 : 45.498503308823544\n",
      "20051024 : 43.03632103321031\n",
      "20051025 : 42.0476648148148\n",
      "20051026 : 41.10723296296297\n",
      "20051027 : 39.11501048689141\n",
      "20051028 : 36.52811766917293\n",
      "20051031 : 28.84211264367815\n",
      "20051101 : 28.6426724137931\n",
      "20051102 : 29.12321532567051\n",
      "20051103 : 28.766580076628358\n",
      "20051104 : 28.990105363984657\n",
      "20051107 : 29.06332720306513\n",
      "20051108 : 29.204245210727972\n",
      "20051109 : 29.127599233716456\n",
      "20051110 : 28.498008045977006\n",
      "20051111 : 28.59871226053639\n",
      "20051114 : 28.56729463601532\n",
      "20051115 : 28.305070498084294\n",
      "20051116 : 28.49445325670498\n",
      "20051117 : 28.49096206896553\n",
      "20051118 : 29.13842567049809\n",
      "20051121 : 29.19170613026819\n",
      "20051122 : 28.577254789272033\n",
      "20051123 : 28.753838697317992\n",
      "20051124 : 28.87063065134097\n",
      "20051125 : 28.84406513409961\n",
      "20051128 : 28.70659885057472\n",
      "20051129 : 28.45251264367815\n",
      "20051130 : 28.443810344827583\n",
      "20051201 : 28.226745977011483\n",
      "20051202 : 28.05970114942527\n",
      "20051205 : 27.630441762452097\n",
      "20051206 : 27.755520306513404\n",
      "20051207 : 27.96881647509577\n",
      "20051208 : 27.953449808429117\n",
      "20051209 : 28.37220842911875\n",
      "20051212 : 28.492860536398478\n",
      "20051213 : 28.4793295019157\n",
      "20051214 : 28.81326781609197\n",
      "20051215 : 28.365747126436787\n",
      "20051216 : 28.62947241379311\n",
      "20051219 : 28.605332183908043\n",
      "20051220 : 28.667523754789276\n",
      "20051221 : 28.476468199233718\n",
      "20051222 : 28.65250766283526\n",
      "20051223 : 28.91844904214559\n",
      "20051226 : 29.125627203065132\n",
      "20051227 : 29.046282375478935\n",
      "20051228 : 29.045399233716466\n",
      "20051229 : 29.35565938697318\n",
      "20051230 : 29.130057854406136\n",
      "20060104 : 29.753843396226408\n",
      "20060105 : 30.171392452830183\n",
      "20060106 : 30.464188679245286\n",
      "20060109 : 30.792619622641524\n",
      "20060110 : 30.825888679245278\n",
      "20060111 : 30.7081883018868\n",
      "20060112 : 31.263005283018877\n",
      "20060113 : 31.227733962264153\n",
      "20060116 : 30.702016603773576\n",
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     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "ittm_pe_df = pd.DataFrame(columns=['date', 'pe_ttm', 'profit', 'loss'])  \n",
    "\n",
    "for index,row in trade_date.iterrows():\n",
    "    filterstr = \"index_code=000300.SH&start_date=\" + row['trade_date'] + \"&end_date=\" + row['trade_date']\n",
    "    df, msg = api.query(\n",
    "        view = 'lb.indexCons', \n",
    "        filter=filterstr\n",
    "    )\n",
    "    symbols = \",\".join(df['symbol'])\n",
    "\n",
    "    filterstr2 = \"start_date=\" + row['trade_date'] + \"&end_date=\" + row['trade_date'] + \"&symbol=\" + symbols\n",
    "    df_data, msg = api.query(\n",
    "        view = 'lb.secDailyIndicator', \n",
    "        filter=filterstr2, \n",
    "        fields='total_mv,float_mv,pe_ttm,pb,trade_date'\n",
    "    )\n",
    "    df_data.set_index('symbol', inplace=True)\n",
    "#     print(df_data)\n",
    "    index_pe_ttm = df_data[df_data.pe_ttm > 0]['pe_ttm'].sum() / len(df_data[df_data.pe_ttm > 0])\n",
    "#     print(index_pe_ttm)\n",
    "    ittm_pe_df = ittm_pe_df.append({\"date\":row['trade_date'], \"pe_ttm\":index_pe_ttm, \"profit\":len(df_data[df_data.pe_ttm > 0]), \"loss\": len(df_data[df_data.pe_ttm <= 0])}, ignore_index=True)\n",
    "#     print(ittm_pe_df)\n",
    "    print(row['trade_date'] + \" : \" + str(index_pe_ttm))\n",
    "#     break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BDDXmINDStGsLV5uvc7wLvAuqsFtl+qGpBfihoQeL4vX8jfXY2Nia6M4Zya6M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\nRqNpgPw/kfZDTTaCY1EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb476210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ittm_pe_df = ittm_pe_df.set_index('date')\n",
    "ittm_pe_df.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3221"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ittm_pe_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               float_mv       pb    pe_ttm      total_mv trade_date\n",
      "symbol                                                             \n",
      "000001.SZ  1.918499e+07   0.9634    8.3968  1.947125e+07   20180323\n",
      "000002.SZ  3.016638e+07   2.9473   14.3802  3.429865e+07   20180323\n",
      "000008.SZ  1.803303e+06   3.1824   46.5438  1.986923e+06   20180323\n",
      "000060.SZ  2.007669e+06   2.1862   21.1362  2.160850e+06   20180323\n",
      "000063.SZ  9.961989e+06   2.9689   26.6254  1.216294e+07   20180323\n",
      "000069.SZ  5.003878e+06   1.3918    7.9229  6.678122e+06   20180323\n",
      "000100.SZ  4.093402e+06   1.6474   23.9413  4.716725e+06   20180323\n",
      "000157.SZ  2.627133e+06   0.8723   28.5779  3.289088e+06   20180323\n",
      "000166.SZ  9.913919e+06   1.6782   23.9761  1.115529e+07   20180323\n",
      "000333.SZ  3.575806e+07   5.1808   21.9149  3.698032e+07   20180323\n",
      "000338.SZ  3.437864e+06   1.6656   11.4902  6.365802e+06   20180323\n",
      "000402.SZ  2.918675e+06   1.0429    8.4779  2.920185e+06   20180323\n",
      "000413.SZ  3.038212e+06   1.3980   29.0386  4.251846e+06   20180323\n",
      "000415.SZ  1.819502e+06   1.1556   13.3277  3.611760e+06   20180323\n",
      "000423.SZ  3.936281e+06   3.9995   19.2590  3.937210e+06   20180323\n",
      "000425.SZ  2.762458e+06   1.1616   33.7127  2.768052e+06   20180323\n",
      "000503.SZ  3.437096e+06  27.2868    0.0000  3.437096e+06   20180323\n",
      "000538.SZ  1.049203e+07   5.9963   33.1498  1.049210e+07   20180323\n",
      "000540.SZ  3.178410e+06   2.1978   13.5776  3.452784e+06   20180323\n",
      "000559.SZ  2.332945e+06   4.8989   28.0487  2.337432e+06   20180323\n",
      "000568.SZ  7.983091e+06   5.6966   34.4878  8.341765e+06   20180323\n",
      "000623.SZ  2.174176e+06   1.1365   12.3420  2.308098e+06   20180323\n",
      "000625.SZ  4.197143e+06   1.1567    6.4139  5.359756e+06   20180323\n",
      "000627.SZ  1.026016e+06   1.9953   21.1019  3.744997e+06   20180323\n",
      "000630.SZ  2.495280e+06   1.6089   57.1827  2.747425e+06   20180323\n",
      "000651.SZ  2.918376e+07   5.0159   14.9623  2.940489e+07   20180323\n",
      "000671.SZ  2.362772e+06   2.0032   19.8501  2.948453e+06   20180323\n",
      "000686.SZ  1.809170e+06   1.1457   22.7158  1.809170e+06   20180323\n",
      "000709.SZ  3.726531e+06   0.8126   20.0423  3.727131e+06   20180323\n",
      "000723.SZ  5.654908e+05   2.8504   18.3493  2.147402e+06   20180323\n",
      "...                 ...      ...       ...           ...        ...\n",
      "601788.SH  4.688039e+06   1.1365   18.2468  5.532945e+06   20180323\n",
      "601800.SH  1.511869e+07   1.3090   12.1043  2.081688e+07   20180323\n",
      "601818.SH  1.668058e+07   0.8037    7.0209  2.199297e+07   20180323\n",
      "601857.SH  1.267850e+08   1.2004   62.8726  1.433054e+08   20180323\n",
      "601866.SH  2.379638e+06   2.4429   16.3554  3.504938e+06   20180323\n",
      "601872.SH  1.742020e+06   1.2589   29.5105  1.955500e+06   20180323\n",
      "601877.SH  4.177082e+06   2.7496   18.4145  5.283908e+06   20180323\n",
      "601878.SH  4.073334e+05   3.0142   38.3009  4.073333e+06   20180323\n",
      "601881.SH  1.239588e+06   1.5942   20.5083  1.022849e+07   20180323\n",
      "601888.SH  1.113887e+07   7.9379   44.3279  1.113887e+07   20180323\n",
      "601898.SH  4.704128e+06   0.7656   28.2260  6.814953e+06   20180323\n",
      "601899.SH  7.174927e+06   3.0519   39.7651  1.045617e+07   20180323\n",
      "601901.SH  4.889868e+06   1.3064   33.6546  4.889868e+06   20180323\n",
      "601919.SH  4.703575e+06   2.9869   30.6987  6.293225e+06   20180323\n",
      "601933.SH  6.417747e+06   4.7170   52.3764  9.436476e+06   20180323\n",
      "601939.SH  7.444678e+06   1.1600    8.1220  1.940085e+08   20180323\n",
      "601958.SH  2.148919e+06   1.6850  198.7009  2.148919e+06   20180323\n",
      "601966.SH  6.823120e+05   2.3896   20.0639  2.076000e+06   20180323\n",
      "601985.SH  2.838095e+06   2.4042   23.2536  1.044440e+07   20180323\n",
      "601988.SH  8.683539e+07   0.8779    6.9200  1.212878e+08   20180323\n",
      "601989.SH  9.291003e+06   1.3511  128.1209  1.157718e+07   20180323\n",
      "601991.SH  3.657936e+06   1.4135   24.2435  5.894423e+06   20180323\n",
      "601992.SH  3.771357e+06   1.0725   16.3671  4.943808e+06   20180323\n",
      "601997.SH  1.791544e+06   1.3449    7.3372  3.323764e+06   20180323\n",
      "601998.SH  2.144027e+07   0.9017    7.7255  3.288418e+07   20180323\n",
      "603160.SH  1.885377e+06  11.1669   36.6561  3.728649e+06   20180323\n",
      "603799.SH  6.803257e+06  13.0215   59.5640  6.885124e+06   20180323\n",
      "603833.SH  5.538679e+05   9.1559   47.5814  5.612019e+06   20180323\n",
      "603858.SH  1.622784e+06   2.6712   20.5668  3.374910e+06   20180323\n",
      "603993.SH  1.077750e+07   4.8206   89.0038  1.797057e+07   20180323\n",
      "\n",
      "[300 rows x 5 columns] 0,\n"
     ]
    }
   ],
   "source": [
    "df, msg = api.query(\n",
    "    view = 'lb.indexCons', \n",
    "    filter=\"index_code=000300.SH&start_date=20180323&end_date=20180323\"\n",
    ")\n",
    "symbols = \",\".join(df['symbol'])\n",
    "\n",
    "df_data, msg = api.query(\n",
    "    view = 'lb.secDailyIndicator', \n",
    "    filter=\"start_date=20180323&end_date=20180323&symbol=\"+symbols, \n",
    "    fields='total_mv,float_mv,pe_ttm,pb,trade_date'\n",
    ")\n",
    "df_data.set_index('symbol', inplace=True)\n",
    "print(df_data, msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>float_mv</th>\n",
       "      <th>pb</th>\n",
       "      <th>pe_ttm</th>\n",
       "      <th>total_mv</th>\n",
       "      <th>trade_date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000503.SZ</th>\n",
       "      <td>3.437096e+06</td>\n",
       "      <td>27.2868</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.437096e+06</td>\n",
       "      <td>20180323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000792.SZ</th>\n",
       "      <td>3.286123e+06</td>\n",
       "      <td>1.4143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.329378e+06</td>\n",
       "      <td>20180323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600100.SH</th>\n",
       "      <td>2.904621e+06</td>\n",
       "      <td>1.3907</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.904621e+06</td>\n",
       "      <td>20180323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600685.SH</th>\n",
       "      <td>1.081642e+06</td>\n",
       "      <td>2.7448</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.783194e+06</td>\n",
       "      <td>20180323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600871.SH</th>\n",
       "      <td>2.673471e+06</td>\n",
       "      <td>3.2485</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.214523e+06</td>\n",
       "      <td>20180323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601608.SH</th>\n",
       "      <td>1.436205e+06</td>\n",
       "      <td>2.0281</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.449366e+06</td>\n",
       "      <td>20180323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               float_mv       pb  pe_ttm      total_mv trade_date\n",
       "symbol                                                           \n",
       "000503.SZ  3.437096e+06  27.2868     0.0  3.437096e+06   20180323\n",
       "000792.SZ  3.286123e+06   1.4143     0.0  3.329378e+06   20180323\n",
       "600100.SH  2.904621e+06   1.3907     0.0  2.904621e+06   20180323\n",
       "600685.SH  1.081642e+06   2.7448     0.0  2.783194e+06   20180323\n",
       "600871.SH  2.673471e+06   3.2485     0.0  4.214523e+06   20180323\n",
       "601608.SH  1.436205e+06   2.0281     0.0  1.449366e+06   20180323"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data[df_data.pe_ttm <= 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "294"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_data[df_data.pe_ttm > 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.95310986394558"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data[df_data.pe_ttm > 0]['pe_ttm'].sum() / len(df_data[df_data.pe_ttm > 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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